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Glama

Latam Validate

by io.github.pipeworx-io

Entity Profile

entity_profile
Read-onlyIdempotent

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when value was a name); recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. sources_used / sources_failed say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure. type accepts "company" or "ticker" interchangeably — both take the same value shapes above.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes"company" or "ticker" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon.
valueYesTicker (e.g., "AAPL"), zero-padded CIK (e.g., "0000320193"), or company name (e.g., "Moderna") — names resolve via SEC EDGAR company-name match.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. Only \"company\" supported today; person/place coming soon."New value: +"\"company\" or \"ticker\" — both are accepted and behave identically; `value` can be a ticker, CIK, or company name either way. person/place coming soon."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "ticker"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker (e.g., \"AAPL\") or zero-padded CIK (e.g., \"0000320193\"). Names not supported — use resolve_entity first if you only have a name."New value: +"Ticker (e.g., \"AAPL\"), zero-padded CIK (e.g., \"0000320193\"), or company name (e.g., \"Moderna\") — names resolve via SEC EDGAR company-name match."
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses source fan-out, the GDELT→GNews fallback, the USPTO sunset soft-fail, the meaning of empty sections via sources_used/sources_failed, and the resolved:false behavior for private companies. This is rich behavioral context that annotations alone could not convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded with trigger phrases and the usage preference, and nearly every sentence carries useful information. It loses one point for redundancy with the schema—the type/value interchangeability is stated in both places—and for being a long single paragraph that is somewhat heavy to skim.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: cik, company_name, resolved fields, recent_filings with URIs, fundamentals, sources_used/sources_failed, and explicit no-data semantics. The edge cases a caller would care about are all covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds meaningful parameter nuance: zero-padded CIK examples, ticker/CIK/name equivalence, SEC EDGAR name resolution, resolved_from/resolved_to, and private-company resolved:false behavior. This substantially exceeds the baseline expected for fully documented schemas.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a full cross-source profile of a US public company, backed by concrete trigger phrasing like 'brief me on Tesla' and 'company profile for Microsoft'. It distinguishes itself from piecemeal lookups by emphasizing the single parallel call and holistic scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' giving an unambiguous selection rule. It also specifies accepted input forms and explains the private-company failure mode, so an agent knows when the tool can and cannot be used.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Latam Validate toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same 5,702 tools with only subtle differences, and ai_visibility_check is essentially a single-entity subset of scan_competitor_ai_presence. The memory trio (remember/recall/forget) and subscription tools also sit awkwardly alongside the data-query tools, making selection genuinely ambiguous.

Naming Consistency2/5

Naming mixes verb_noun (validate_cnpj, resolve_entity, compare_entities), noun_verb (bet_research, entity_profile, recent_changes), and bare nouns with inconsistent suffixes (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk). The ask_pipeworx family uses inconsistent qualifiers (beta vs grounded), and validate_* is used for both checksum-only tools and full lookups.

Tool Count2/5

36 tools is excessive for a server ostensibly named 'Latam Validate' — only 5 tools relate to LATAM validation while the rest form a sprawling general-purpose data and prediction-market platform. Many tools could be consolidated (the ask_pipeworx family, the polymarket_* family, the memory trio), suggesting the set is over-scoped for any single agent's typical workflow.

Completeness2/5

For the stated LATAM validation purpose, coverage is thin: only Brazil (CPF/CNPJ/CEP/banks) and Mexico (CLABE) are covered, with no validators for other LATAM jurisdictions. For the broader Pipeworx data platform, the surface is extensive but has notable gaps — grounded retrieval, claim verification, and arbitrage tools exist, yet many LatAm-specific data sources and common validation formats (RFC, RUT, DNI, CURP) are absent.

Resources